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output.txt

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+trelis_0.wav|I'm going to walk you through 10 quick tips for fine tuning. For each of those, I'll point you to one or two trellis videos on YouTube and also point you to the right branch if you're working out of the trellis advanced fine tuning repository. Tip number one is to start with a small model. I recommend starting with something like Lama 3 8B or Phi 3 Mini.|1
+trelis_1.wav|This one, I use a relatively small model as I do in many of my fine tuning tutorials, just because it's quicker to learn fast. Tip number two is to use LoRa or QLoRa. I don't recommend starting off with full fine-tuning for a few reasons. First of all, LoRa and QLoRa allow you to start with fewer GPUs or a smaller GPU. That's going to make iteration faster.|1
+trelis_2.wav|So you want to create 10 question answer pairs and use those to choose which base model is going to perform best. So just by running those on different base models, you can see which one is going to give you the best baseline for starting off your fine tuning. Then after you do any training run, you want to run that manual test. and just evaluate whether the model is doing well.|1
+trelis_3.wav|This gives you probably a better sense than solely looking at the eval and training loss during the fine-tuning process. This is what I do in this memorization video as well, which you can check out on YouTube, and you'll see in the memorization scripts how I allow you to set up this manual dataset. That's also possible in the unsloth branch and the multi-GPU branch, which I'll get to later.|1
+trelis_4.wav|Tip number four is to create data sets manually. Yes, I know this is a bit of work, but I think it's underrated. When you manually curate a data set like I did for the trellis function calling data set, it lets you appreciate exactly which rows of data are needed to get the performance that you need. You can, of course, use Python and chat GPT to help automate some of this and generate rows.|1
+trelis_5.wav|If you do want to automate a little more how you generate synthetic data sets, you can check out this video here on data set preparation with LLMs. Tip number six is always use a validation data set. If you don't have one, you can just split off 10 to 20% of your training data set. You want to be checking your training loss as you progress along the process.|1
+trelis_6.wav|Then, as a very last step, you can think about moving to a larger model where it's going to take more time and money to get in that final result. There are two videos of relevance here. If you want to understand the pros and cons of full fine-tuning versus QLORA or LoRa, take a look at this video. And if you want to understand the complexities of doing multi-GPU training, check out multi-GPU fine-tuning.|1
+trelis_7.wav|Moving to two last tips, tip number nine is to use unsupervised fine tuning. This can be useful if you have a large data set. I'm going to say larger than 10,000 rows of data. Here, you'll need to use Python scripts in order to clean up, say, repeated characters or too much new lines. You can also use language models in order to clean up the data set chunk by chunk.|1
+trelis_8.wav|The video of relevance here is the Wikipedia video I made, where I first extract data from Wikipedia, clean it, and then use it for fine tuning. Last of all, my tip number 10 is to do preference fine-tuning. This is where you have a data set with chosen, which are better or preferred responses, and rejected, which are the responses to the same prompts but are of lower quality.|1
+trelis_9.wav|The preference fine-tuning will move your model to give responses more like your chosen answers and less like your rejected answers, which is useful if you want to do some fine-tuning for tone or style, or if you want to make some corrections where the model's giving a response you don't quite like. Here I recommend the Orpo YouTube video, and there's also a branch by that name in Advanced Fine Tuning.|1
+trelis_10.wav|Orpo is also supported in the Unslot branch, where there's a Python Jupyter notebook and also just a Python.py script you can run. And Orpo is supported as an option in the Multi-GPU branch too. So to recap these 10 tips, start with a small model, use LoRa or QLoRa, not full fine-tuning. Always create 10 manual test questions or maybe a few more. Remember that manual data sets are probably underrated.|1
+trelis_11.wav|You can always get a little bit of help from Python or from chat GPT. Start training on a small number of rows, even just one row to test the pipeline, but then 100, and make sure it's having a good effect before you decide to scale up. Make sure you know that the data type and the data set that you've set up is actually the right one.|1
+trelis_12.wav|Number six, always use a validation set. Just split one off from a training set if you don't have one. Number seven, try to just start training on one GPU. Number eight, use weights and biases for tracking. And when you're scaling from small to large, increase first the rows, then move to using more VRAM with LoRa instead of QLoRa or full fine tuning instead of LoRa.|1
+trelis_13.wav|By the way, there's a factor of four roughly in VRAM difference between each of those. So LoRa is about four times QLoRa and full fine tuning is about four times. LoRa, or even more in some cases. And last of all, increase to a larger model size only at the very end of your training process when you think you have a pipeline that's working well.|1
+trelis_14.wav|Now, this approach here I've talked about for language models, but it also works for video and speech or images, multimodal models. So you can check out this video here on multimodal text plus image, where I prepare a data set and bring it through fine tuning. And likewise, for this speech to text model, where I prepare a data set and bring it through fine tuning.|1
+trelis_15.wav|There are specific repos for multimodal. That's the vision repository here. And there's a repo for transcription. And this LLMs repo is the advanced fine-tuning repo I've been talking to date in or up until now in this presentation. I've laid out here all of the playlists that are relevant depending on what you need. So there are four different sections, four playlists and four repositories that go with them.|1
+trelis_16.wav|This very last section of the video is for those who have purchased lifetime access to one of the Trellis repositories, but I'll just put it part of this public video because it will give a sense of what's in these repositories for those of you who might be interested to purchase lifetime membership later. The first repo is the advanced fine-tuning repo, and this is split into branches according to function.|1
+trelis_17.wav|Now, the notebook is recommended if you want to go through the training the first time, you can see step by step what's happening and easily print out things at intermediate points. But when you've got your script honed, it can be a lot faster to run a Python script. And that's why I have made this script available, which you just run from the command line and it will go through everything within the training.|1
+trelis_18.wav|Just to give you a sense of how you configure the training and test setup, you'll set a model slug. You will then set some parameters, like whether you want to fine tune in 4-bit, what data type you want to use, depending on your GPU. You can then choose a data set, say for function calling, or if you want to memorize some data, like on the rules of TouchRugby.|1
+trelis_19.wav|Check out the live stream video on choosing LoRa parameters if you want to know more. You can set LoRa or LoRa alpha and also rank stabilize LoRa, set that to true or false. Here you've got some Weights and Biases project configurations. You set the project name, and then for each run, you can use a different name here for running in Weights and Biases.|1
+trelis_20.wav|And this can be useful if your answers are quite short and you don't want the loss on all of the prompts to kind of crowd out or cloud out the information or the signal that's coming from training on the response or the answer. So you set the completions to true here. Sometimes I use this for function calling, fine tuning. And then you need to let the model know where your answer is starting.|1
+trelis_21.wav|the number of epochs, the learning rate, an output directory for your training model and results, whether you want to train with BrainFloat 16 or not. You can set your scheduler. You can decide whether to save the model at a certain number of steps of training. set your max sequence length, gradient checkpointing, and whether to use re-entrancy, which allows you to speed up the training.|1
+trelis_22.wav|Next, you can decide whether you want to use ORPO or not. By default, I've got that set to false. If you're using ORPO, you need a column that's called chosen and one called rejected. and you can set your max prompt length and then the beta. The beta basically weighs how much of the preference fine-tuning, what's the importance of that loss relative to the standard SFT loss.|1
+trelis_23.wav|It will set up the tokenizer, set up the chat template, load the dataset, either from your manual data that's in the repo or from Hugging Face, and then it will run inference through all of those samples and print the results out to file. Just as an example, I can show you within test output, you'll see here a large number of tests that I have run.|1
+trelis_24.wav|It has the test.py and the train.py file that will allow you to run testing and training. And I'll just briefly show you the config file. So at the start here, you'll see this parameter that's not in the unsloth branch. If you set it to auto, it will just do standard training. You can train on multiple GPUs, but it will be pipeline parallel, so not quite as efficient.|1
+trelis_25.wav|Then you can fine tune Lava, IdaFix and, or IdaFix and Moondream models. You can do multimodal server setup with text generation inference. There's a one-click template for running an IdaFix server, including on a custom model. And last of all, there is a script for fine-tuning multimodal text plus video models. This is basically a variation on text plus image models where you split the video into multiple images.|1
+trelis_26.wav|So the idea is to use a very fast and relatively small language model to pick out the right snippets and then include those snippets in the context of a more powerful model like, say, GPT-4. There's also a folder now on privacy, which allows you to basically hide information, like personal information on credit cards, names, email addresses, before you send it to a third-party API so that you can reduce any data privacy risks.|1
+trelis_27.wav|Last of all, there's the advanced transcription repository. This one here allows you to generate data if you want to fine tune a whisper model and then do the fine tuning. And again, much of the 10 tips that I provided earlier are going to apply here for transcription. And that is it for my 10 tips on fine-tuning. If I've left anything out, please let me know below in the comments and I'll get back to you.|1

train_list.txt

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+trelis_0.wav|aɪm ɡˌoʊɪŋ tə wˈɔːk juː θɹuː tˈɛn kwˈɪk tˈɪps fɔːɹ fˈaɪn tˈuːnɪŋ. fɔːɹ ˈiːtʃ əv ðˈoʊz, aɪl pˈɔɪnt juː tə wˈʌn ɔːɹ tˈuː tɹˈɛliz vˈɪdɪoʊz ˌɔn juː tˈuːb ænd ˈɔːlsoʊ pˈɔɪnt juː tə ðə ɹˈaɪt bɹˈæntʃ ɪf jʊɹ wˈɜːkɪŋ ˌaʊɾəv ðə tɹˈɛliz ɐdvˈænst fˈaɪn tˈuːnɪŋ ɹᵻpˈɑːzɪtˌoːɹi. tˈɪp nˈʌmbɚ wˈʌn ɪz tə stˈɑːɹt wɪð ɐ smˈɔːl mˈɑːdəl. aɪ ɹˌɛkəmˈɛnd stˈɑːɹɾɪŋ wɪð sˈʌmθɪŋ lˈaɪk lˈɑːmə θɹˈiː ˈeɪt bˈiː ɔːɹ fˈaɪ θɹˈiː mˈɪni. |1
+trelis_1.wav|ðˈɪswˌʌn, aɪ jˈuːz ɐ ɹˈɛlətˌɪvli smˈɔːl mˈɑːdəl æz aɪ dˈuː ɪn mˈɛnɪəv maɪ fˈaɪn tˈuːnɪŋ tuːtˈoːɹɪəlz, dʒˈʌst bɪkˈʌz ɪts kwˈɪkɚ tə lˈɜːn fˈæst. tˈɪp nˈʌmbɚ tˈuː ɪz tə jˈuːz lˈoʊ ɹˈɑː ɔːɹ kjˈuː lˈoʊ ɹˈɑː. aɪ dˈoʊnt ɹˌɛkəmˈɛnd stˈɑːɹɾɪŋ ˈɔf wɪð fˈʊl fˈaɪntˈuːnɪŋ fɚɹə fjˈuː ɹˈiːzənz. fˈɜːst ʌv ˈɔːl, lˈoʊ ɹˈɑː ænd kjˈuː lˈoʊ ɹˈɑː ɐlˈaʊ juː tə stˈɑːɹt wɪð fjˈuːɚ dʒˌiːpˌiːjˈuː ɔːɹ ɐ smˈɔːlɚ dʒˌiːpˌiːjˈuː. ðæts ɡˌoʊɪŋ tə mˌeɪk ˌɪɾɚɹˈeɪʃən fˈæstɚ. |1
+trelis_2.wav|sˌoʊ juː wˈɔnt tə kɹiːˈeɪt tˈɛn kwˈɛstʃən ˈænsɚ pˈɛɹz ænd jˈuːs ðoʊz tə tʃˈuːz wˌɪtʃ bˈeɪs mˈɑːdəl ɪz ɡˌoʊɪŋ tə pɚfˈɔːɹm bˈɛst. sˌoʊ dʒˈʌst baɪ ɹˈʌnɪŋ ðoʊz ˌɔn dˈɪfɹənt bˈeɪs mˈɑːdəlz, juː kæn sˈiː wˌɪtʃ wˈʌn ɪz ɡˌoʊɪŋ tə ɡˈɪv juː ðə bˈɛst bˈeɪslaɪn fɔːɹ stˈɑːɹɾɪŋ ˈɔf jʊɹ fˈaɪn tˈuːnɪŋ. ðˈɛn ˈæftɚ juː dˈuː ˌɛni tɹˈeɪnɪŋ ɹˈʌn, juː wˈɔnt tə ɹˈʌn ðæt mˈænjuːəl tˈɛst. ænd dʒˈʌst ɪvˈæljuːˌeɪt wˈɛðɚ ðə mˈɑːdəl ɪz dˌuːɪŋ wˈɛl. |1
+trelis_3.wav|ðɪs ɡˈɪvz juː pɹˈɑːbəbli ɐ bˈɛɾɚ sˈɛns ðɐn sˈoʊlli lˈʊkɪŋ æt ðɪ ɪvˈæl ænd tɹˈeɪnɪŋ lˈɔs dˈʊɹɹɪŋ ðə fˈaɪntˈuːnɪŋ pɹˈɑːsɛs. ðɪs ɪz wʌt aɪ dˈuː ɪn ðɪs mˌɛmɚɹᵻzˈeɪʃən vˈɪdɪoʊ æz wˈɛl, wˌɪtʃ juː kæn tʃˈɛk ˈaʊt ˌɔn juː tˈuːb, ænd juːl sˈiː ɪnðə mˌɛmɚɹᵻzˈeɪʃən skɹˈɪpts hˌaʊ aɪ ɐlˈaʊ juː tə sˈɛt ˌʌp ðɪs mˈænjuːəl dˈeɪɾəsˌɛt. ðæts ˈɔːlsoʊ pˈɑːsᵻbəl ɪnðɪ ʌnslˈɑːθ bɹˈæntʃ ænd ðə mˈʌltaɪdʒˌiːpˌiːjˈuː bɹˈæntʃ, wˌɪtʃ aɪl ɡɛt tə lˈeɪɾɚ. |1
+trelis_4.wav|tˈɪp nˈʌmbɚ fˈoːɹ ɪz tə kɹiːˈeɪt dˈeɪɾə sˈɛts mˈænjuːəli. jˈɛs, aɪ nˈoʊ ðɪs ɪz ɐ bˈɪt ʌv wˈɜːk, bˌʌt aɪ θˈɪŋk ɪts ˌʌndɚɹˈeɪɾᵻd. wˌɛn juː mˈænjuːəli kjˈʊɹɹeɪt ɐ dˈeɪɾə sˈɛt lˈaɪk aɪ dˈɪd fɚðə tɹˈɛliz fˈʌŋkʃən kˈɔːlɪŋ dˈeɪɾə sˈɛt, ɪt lˈɛts juː ɐpɹˈiːʃɪˌeɪt ɛɡzˈæktli wˌɪtʃ ɹˈoʊz ʌv dˈeɪɾə ɑːɹ nˈiːdᵻd tə ɡɛt ðə pɚfˈoːɹməns ðæt juː nˈiːd. juː kˈæn, ʌv kˈoːɹs, jˈuːs pˈaɪθən ænd tʃˈæt dʒˌiːpˌiːtˈiː tə hˈɛlp ˈɔːɾəmˌeɪt sˌʌm ʌv ðɪs ænd dʒˈɛnɚɹˌeɪt ɹˈoʊz. |1
+trelis_5.wav|ɪf juː dˈuː wˈɔnt tʊ ˈɔːɾəmˌeɪt ɐ lˈɪɾəl mˈoːɹ hˌaʊ juː dʒˈɛnɚɹˌeɪt sɪnθˈɛɾɪk dˈeɪɾə sˈɛts, juː kæn tʃˈɛk ˈaʊt ðɪs vˈɪdɪoʊ hˈɪɹ ˌɔn dˈeɪɾə sˈɛt pɹˌɛpɚɹˈeɪʃən wɪð ˌɛlˌɛlˈɛm. tˈɪp nˈʌmbɚ sˈɪks ɪz ˈɔːlweɪz jˈuːs ɐ vˌælɪdˈeɪʃən dˈeɪɾə sˈɛt. ɪf juː dˈoʊnt hˈæv wˌʌn, juː kæn dʒˈʌst splˈɪt ˈɔf tˈɛn tə twˈɛnti pɚsˈɛnt ʌv jʊɹ tɹˈeɪnɪŋ dˈeɪɾə sˈɛt. juː wˈɔnt təbi tʃˈɛkɪŋ jʊɹ tɹˈeɪnɪŋ lˈɔs æz juː pɹəɡɹˈɛs ɐlˈɔŋ ðə pɹˈɑːsɛs. |1
+trelis_6.wav|ðˈɛn, æz ɐ vˈɛɹi lˈæst stˈɛp, juː kæn θˈɪŋk ɐbˌaʊt mˈuːvɪŋ tʊ ɐ lˈɑːɹdʒɚ mˈɑːdəl wˌɛɹ ɪts ɡˌoʊɪŋ tə tˈeɪk mˈoːɹ tˈaɪm ænd mˈʌni tə ɡɛt ɪn ðæt fˈaɪnəl ɹɪzˈʌlt. ðɛɹˌɑːɹ tˈuː vˈɪdɪoʊz ʌv ɹˈɛlᵻvəns hˈɪɹ. ɪf juː wˈɔnt tʊ ˌʌndɚstˈænd ðə pɹˈoʊz ænd kˈɑːnz ʌv fˈʊl fˈaɪntˈuːnɪŋ vˈɜːsᵻz kjˈuːlˈoːɹə ɔːɹ lˈoʊ ɹˈɑː, tˈeɪk ɐ lˈʊk æt ðɪs vˈɪdɪoʊ. ænd ɪf juː wˈɔnt tʊ ˌʌndɚstˈænd ðə kəmplˈɛksᵻɾiz ʌv dˌuːɪŋ mˈʌltaɪdʒˌiːpˌiːjˈuː tɹˈeɪnɪŋ, tʃˈɛk ˈaʊt mˈʌltaɪdʒˌiːpˌiːjˈuː fˈaɪntˈuːnɪŋ. |1
+trelis_7.wav|mˈuːvɪŋ tə tˈuː lˈæst tˈɪps, tˈɪp nˈʌmbɚ nˈaɪn ɪz tə jˈuːz ʌnsˈuːpɚvˌaɪzd fˈaɪn tˈuːnɪŋ. ðɪs kæn biː jˈuːsfəl ɪf juː hæv ɐ lˈɑːɹdʒ dˈeɪɾə sˈɛt. aɪm ɡˌoʊɪŋ tə sˈeɪ lˈɑːɹdʒɚ ðɐn tˈɛn,zˈiəɹoʊzˈiəɹoʊ zˈiəɹoʊ ɹˈoʊz ʌv dˈeɪɾə. hˈɪɹ, juːl nˈiːd tə jˈuːz pˈaɪθən skɹˈɪpts ɪn ˈɔːɹdɚ tə klˈiːn ˈʌp, sˈeɪ, ɹᵻpˈiːɾᵻd kˈæɹɪktɚz ɔːɹ tˈuː mʌtʃ nˈuː lˈaɪnz. juː kæn ˈɔːlsoʊ jˈuːs lˈæŋɡwɪdʒ mˈɑːdəlz ɪn ˈɔːɹdɚ tə klˈiːn ˌʌp ðə dˈeɪɾə sˈɛt tʃˈʌŋk baɪ tʃˈʌŋk. |1
+trelis_8.wav|ðə vˈɪdɪoʊ ʌv ɹˈɛlᵻvəns hˈɪɹ ɪz ðə wˌɪkipˈiːdiə vˈɪdɪoʊ aɪ mˈeɪd, wˌɛɹ aɪ fˈɜːst ˈɛkstɹækt dˈeɪɾə fɹʌm wˌɪkipˈiːdiə, klˈiːn ɪt, ænd ðˈɛn jˈuːz ɪt fɔːɹ fˈaɪn tˈuːnɪŋ. lˈæst ʌv ˈɔːl, maɪ tˈɪp nˈʌmbɚ tˈɛn ɪz tə dˈuː pɹˈɛfɹəns fˈaɪntˈuːnɪŋ. ðɪs ɪz wˌɛɹ juː hæv ɐ dˈeɪɾə sˈɛt wɪð tʃˈoʊzən, wˌɪtʃ ɑːɹ bˈɛɾɚ ɔːɹ pɹɪfˈɜːd ɹᵻspˈɑːnsᵻz, ænd ɹᵻdʒˈɛktᵻd, wˌɪtʃ ɑːɹ ðə ɹᵻspˈɑːnsᵻz tə ðə sˈeɪm pɹˈɑːmpts bˌʌt ɑːɹ ʌv lˈoʊɚ kwˈɔlᵻɾi. |1
+trelis_9.wav|ðə pɹˈɛfɹəns fˈaɪntˈuːnɪŋ wɪl mˈuːv jʊɹ mˈɑːdəl tə ɡˈɪv ɹᵻspˈɑːnsᵻz mˈoːɹ lˈaɪk jʊɹ tʃˈoʊzən ˈænsɚz ænd lˈɛs lˈaɪk jʊɹ ɹᵻdʒˈɛktᵻd ˈænsɚz, wˌɪtʃ ɪz jˈuːsfəl ɪf juː wˈɔnt tə dˈuː sˌʌm fˈaɪntˈuːnɪŋ fɔːɹ tˈoʊn ɔːɹ stˈaɪl, ɔːɹ ɪf juː wˈɔnt tə mˌeɪk sˌʌm kɚɹˈɛkʃənz wˌɛɹ ðə mˈɑːdəlz ɡˈɪvɪŋ ɐ ɹᵻspˈɑːns juː dˈoʊnt kwˈaɪt lˈaɪk. hˈɪɹ aɪ ɹˌɛkəmˈɛnd ðɪ ˈɔːɹpoʊ juː tˈuːb vˈɪdɪoʊ, ænd ðɛɹz ˈɔːlsoʊ ɐ bɹˈæntʃ baɪ ðæt nˈeɪm ɪn ɐdvˈænst fˈaɪn tˈuːnɪŋ. |1
+trelis_10.wav|ˈɔːɹpoʊ ɪz ˈɔːlsoʊ səpˈoːɹɾᵻd ɪnðɪ ʌnslˈɑːt bɹˈæntʃ, wˌɛɹ ðɛɹz ɐ pˈaɪθən dʒˈʌpaɪɾɚ nˈoʊtbʊk ænd ˈɔːlsoʊ dʒˈʌst ɐ pˈaɪθən.pˈaɪ skɹˈɪpt juː kæn ɹˈʌn. ænd ˈɔːɹpoʊ ɪz səpˈoːɹɾᵻd æz ɐn ˈɑːpʃən ɪnðə mˈʌltaɪdʒˌiːpˌiːjˈuː bɹˈæntʃ tˈuː. sˌoʊ tə ɹᵻkˈæp ðiːz tˈɛn tˈɪps, stˈɑːɹt wɪð ɐ smˈɔːl mˈɑːdəl, jˈuːs lˈoʊ ɹˈɑː ɔːɹ kjˈuː lˈoʊ ɹˈɑː, nˌɑːt fˈʊl fˈaɪntˈuːnɪŋ. ˈɔːlweɪz kɹiːˈeɪt tˈɛn mˈænjuːəl tˈɛst kwˈɛstʃənz ɔːɹ mˈeɪbiː ɐ fjˈuːmˌoːɹ. ɹᵻmˈɛmbɚ ðæt mˈænjuːəl dˈeɪɾə sˈɛts ɑːɹ pɹˈɑːbəbli ˌʌndɚɹˈeɪɾᵻd. |1
+trelis_11.wav|juː kæn ˈɔːlweɪz ɡɛt ɐ lˈɪɾəl bˈɪt ʌv hˈɛlp fɹʌm pˈaɪθən ɔːɹ fɹʌm tʃˈæt dʒˌiːpˌiːtˈiː. stˈɑːɹt tɹˈeɪnɪŋ ˌɔn ɐ smˈɔːl nˈʌmbɚɹ ʌv ɹˈoʊz, ˈiːvən dʒˈʌst wˈʌn ɹˈoʊ tə tˈɛst ðə pˈaɪplaɪn, bˌʌt ðˈɛn wˈʌnhˈʌndɹɪd, ænd mˌeɪk ʃˈʊɹ ɪts hˌævɪŋ ɐ ɡˈʊd ɪfˈɛkt bᵻfˌoːɹ juː dᵻsˈaɪd tə skˈeɪl ˈʌp. mˌeɪk ʃˈʊɹ juː nˈoʊ ðætðə dˈeɪɾə tˈaɪp ænd ðə dˈeɪɾə sˈɛt ðæt juːv sˈɛt ˌʌp ɪz ˈæktʃuːəli ðə ɹˈaɪt wˌʌn. |1
+trelis_12.wav|nˈʌmbɚ sˈɪks, ˈɔːlweɪz jˈuːs ɐ vˌælɪdˈeɪʃən sˈɛt. dʒˈʌst splˈɪt wˈʌn ˈɔf fɹʌm ɐ tɹˈeɪnɪŋ sˈɛt ɪf juː dˈoʊnt hˈæv wˌʌn. nˈʌmbɚ sˈɛvən, tɹˈaɪ tə dʒˈʌst stˈɑːɹt tɹˈeɪnɪŋ ˌɔn wˈʌn dʒˌiːpˌiːjˈuː. nˈʌmbɚɹ ˈeɪt, jˈuːs wˈeɪts ænd bˈaɪəsᵻz fɔːɹ tɹˈækɪŋ. ænd wɛn jʊɹ skˈeɪlɪŋ fɹʌm smˈɔːl tə lˈɑːɹdʒ, ˈɪŋkɹiːs fˈɜːst ðə ɹˈoʊz, ðˈɛn mˈuːv tə jˈuːzɪŋ mˈoːɹ vɹˈæm wɪð lˈoʊ ɹˈɑː ɪnstˈɛd ʌv kjˈuː lˈoʊ ɹˈɑː ɔːɹ fˈʊl fˈaɪn tˈuːnɪŋ ɪnstˈɛd ʌv lˈoʊ ɹˈɑː. |1
+trelis_13.wav|baɪ ðə wˈeɪ, ðɛɹz ɐ fˈæktɚɹ ʌv fˈoːɹ ɹˈʌfli ɪn vɹˈæm dˈɪfɹəns bᵻtwˌiːn ˈiːtʃ əv ðˈoʊz. sˌoʊ lˈoʊ ɹˈɑː ɪz ɐbˌaʊt fˈoːɹ tˈaɪmz kjˈuː lˈoʊ ɹˈɑː ænd fˈʊl fˈaɪn tˈuːnɪŋ ɪz ɐbˌaʊt fˈoːɹ tˈaɪmz. lˈoʊ ɹˈɑː, ɔːɹ ˈiːvən mˈoːɹ ɪn sˌʌm kˈeɪsᵻz. ænd lˈæst ʌv ˈɔːl, ˈɪŋkɹiːs tʊ ɐ lˈɑːɹdʒɚ mˈɑːdəl sˈaɪz ˈoʊnli æt ðə vˈɛɹi ˈɛnd ʌv jʊɹ tɹˈeɪnɪŋ pɹˈɑːsɛs wɛn juː θˈɪŋk juː hæv ɐ pˈaɪplaɪn ðæts wˈɜːkɪŋ wˈɛl. |1
+trelis_14.wav|nˈaʊ, ðɪs ɐpɹˈoʊtʃ hˈɪɹ aɪv tˈɔːkt ɐbˌaʊt fɔːɹ lˈæŋɡwɪdʒ mˈɑːdəlz, bˌʌt ɪɾ ˈɔːlsoʊ wˈɜːks fɔːɹ vˈɪdɪoʊ ænd spˈiːtʃ ɔːɹ ˈɪmɪdʒᵻz, mˌʌltɪmˈoʊdəl mˈɑːdəlz. sˌoʊ juː kæn tʃˈɛk ˈaʊt ðɪs vˈɪdɪoʊ hˈɪɹ ˌɔn mˌʌltɪmˈoʊdəl tˈɛkst plˈʌs ˈɪmɪdʒ, wˌɛɹ aɪ pɹɪpˈɛɹ ɐ dˈeɪɾə sˈɛt ænd bɹˈɪŋ ɪt θɹuː fˈaɪn tˈuːnɪŋ. ænd lˈaɪkwaɪz, fɔːɹ ðɪs spˈiːtʃ tə tˈɛkst mˈɑːdəl, wˌɛɹ aɪ pɹɪpˈɛɹ ɐ dˈeɪɾə sˈɛt ænd bɹˈɪŋ ɪt θɹuː fˈaɪn tˈuːnɪŋ. |1
+trelis_15.wav|ðɛɹˌɑːɹ spəsˈɪfɪk ɹˈiːpoʊz fɔːɹ mˌʌltɪmˈoʊdəl. ðæts ðə vˈɪʒən ɹᵻpˈɑːzɪtˌoːɹi hˈɪɹ. ænd ðɛɹz ɐ ɹˈiːpoʊ fɔːɹ tɹænskɹˈɪpʃən. ænd ðɪs ˌɛlˌɛlˈɛm ɹˈiːpoʊ ɪz ðɪ ɐdvˈænst fˈaɪntˈuːnɪŋ ɹˈiːpoʊ aɪv bˌɪn tˈɔːkɪŋ tə dˈeɪt ɪn ɔːɹ ˌʌp ʌntˈɪl nˈaʊ ɪn ðɪs pɹˌɛzəntˈeɪʃən. aɪv lˈeɪd ˈaʊt hˈɪɹ ˈɔːl ʌvðə plˈeɪlɪsts ðæt ɑːɹ ɹˈɛlᵻvənt dᵻpˈɛndɪŋ ˌɔn wʌt juː nˈiːd. sˌoʊ ðɛɹˌɑːɹ fˈoːɹ dˈɪfɹənt sˈɛkʃənz, fˈoːɹ plˈeɪlɪsts ænd fˈoːɹ ɹᵻpˈɑːzɪtˌoːɹiz ðæt ɡˈoʊ wɪð ðˌɛm. |1
+trelis_16.wav|ðɪs vˈɛɹi lˈæst sˈɛkʃən ʌvðə vˈɪdɪoʊ ɪz fɔːɹ ðoʊz hˌuː hæv pˈɜːtʃɪst lˈaɪftaɪm ˈæksɛs tə wˈʌn ʌvðə tɹˈɛliz ɹᵻpˈɑːzɪtˌoːɹiz, bˌʌt aɪl dʒˈʌst pˌʊt ɪt pˈɑːɹt ʌv ðɪs pˈʌblɪk vˈɪdɪoʊ bɪkˈʌz ɪt wɪl ɡˈɪv ɐ sˈɛns ʌv wʌts ɪn ðiːz ɹᵻpˈɑːzɪtˌoːɹiz fɔːɹ ðoʊz ʌv juː hˌuː mˌaɪt biː ˈɪntɹɛstᵻd tə pˈɜːtʃɪs lˈaɪftaɪm mˈɛmbɚʃˌɪp lˈeɪɾɚ. ðə fˈɜːst ɹˈiːpoʊ ɪz ðɪ ɐdvˈænst fˈaɪntˈuːnɪŋ ɹˈiːpoʊ, ænd ðɪs ɪz splˈɪt ˌɪntʊ bɹˈæntʃᵻz ɐkˈoːɹdɪŋ tə fˈʌŋkʃən. |1
+trelis_17.wav|nˈaʊ, ðə nˈoʊtbʊk ɪz ɹˌɛkəmˈɛndᵻd ɪf juː wˈɔnt tə ɡˌoʊ θɹuː ðə tɹˈeɪnɪŋ ðə fˈɜːst tˈaɪm, juː kæn sˈiː stˈɛp baɪ stˈɛp wʌts hˈæpənɪŋ ænd ˈiːzili pɹˈɪnt ˈaʊt θˈɪŋz æɾ ˌɪntɚmˈiːdiət pˈɔɪnts. bˌʌt wɛn juːv ɡɑːt jʊɹ skɹˈɪpt hˈoʊnd, ɪt kæn biː ɐ lˈɑːt fˈæstɚ tə ɹˈʌn ɐ pˈaɪθən skɹˈɪpt. ænd ðæts wˌaɪ aɪ hæv mˌeɪd ðɪs skɹˈɪpt ɐvˈeɪləbəl, wˌɪtʃ juː dʒˈʌst ɹˈʌn fɹʌmðə kəmˈænd lˈaɪn ænd ɪt wɪl ɡˌoʊ θɹuː ˈɛvɹɪθˌɪŋ wɪðˌɪn ðə tɹˈeɪnɪŋ. |1
+trelis_18.wav|dʒˈʌst tə ɡˈɪv juː ɐ sˈɛns ʌv hˌaʊ juː kənfˈɪɡjɚ ðə tɹˈeɪnɪŋ ænd tˈɛst sˈɛɾʌp, juːl sˈɛt ɐ mˈɑːdəl slˈʌɡ. juː wɪl ðˈɛn sˈɛt sˌʌm pɚɹˈæmɪɾɚz, lˈaɪk wˈɛðɚ juː wˈɔnt tə fˈaɪn tˈuːn ɪn fˈoːɹbˈɪt, wˌʌt dˈeɪɾə tˈaɪp juː wˈɔnt tə jˈuːz, dᵻpˈɛndɪŋ ˌɔn jʊɹ dʒˌiːpˌiːjˈuː. juː kæn ðˈɛn tʃˈuːz ɐ dˈeɪɾə sˈɛt, sˈeɪ fɔːɹ fˈʌŋkʃən kˈɔːlɪŋ, ɔːɹ ɪf juː wˈɔnt tə mˈɛmɚɹˌaɪz sˌʌm dˈeɪɾə, lˈaɪk ɔnðə ɹˈuːlz ʌv tˈʌtʃ ɹˈʌɡbi. |1
+trelis_19.wav|tʃˈɛk ˈaʊt ðə lˈaɪv stɹˈiːm vˈɪdɪoʊ ˌɔn tʃˈuːzɪŋ lˈoʊ ɹˈɑː pɚɹˈæmɪɾɚz ɪf juː wˈɔnt tə nˈoʊ mˈoːɹ. juː kæn sˈɛt lˈoʊ ɹˈɑː ɔːɹ lˈoʊ ɹˈɑː ˈælfə ænd ˈɔːlsoʊ ɹˈæŋk stˈeɪbɪlˌaɪz lˈoʊ ɹˈɑː, sˈɛt ðæt tə tɹˈuː ɔːɹ fˈɔls. hˈɪɹ juːv ɡɑːt sˌʌm wˈeɪts ænd bˈaɪəsᵻz pɹˈɑːdʒɛkt kənfˌɪɡjɚɹˈeɪʃənz. juː sˈɛt ðə pɹˈɑːdʒɛkt nˈeɪm, ænd ðˈɛn fɔːɹ ˈiːtʃ ɹˈʌn, juː kæn jˈuːz ɐ dˈɪfɹənt nˈeɪm hˈɪɹ fɔːɹ ɹˈʌnɪŋ ɪn wˈeɪts ænd bˈaɪəsᵻz. |1
+trelis_20.wav|ænd ðɪs kæn biː jˈuːsfəl ɪf jʊɹ ˈænsɚz ɑːɹ kwˈaɪt ʃˈɔːɹt ænd juː dˈoʊnt wˈɔnt ðə lˈɔs ˌɔn ˈɔːl ʌvðə pɹˈɑːmpts tə kˈaɪnd ʌv kɹˈaʊd ˈaʊt ɔːɹ klˈaʊd ˈaʊt ðɪ ˌɪnfɚmˈeɪʃən ɔːɹ ðə sˈɪɡnəl ðæts kˈʌmɪŋ fɹʌm tɹˈeɪnɪŋ ɔnðə ɹᵻspˈɑːns ɔːɹ ðɪ ˈænsɚ. sˌoʊ juː sˈɛt ðə kəmplˈiːʃənz tə tɹˈuː hˈɪɹ. sˈʌmtaɪmz aɪ jˈuːz ðɪs fɔːɹ fˈʌŋkʃən kˈɔːlɪŋ, fˈaɪn tˈuːnɪŋ. ænd ðˈɛn juː nˈiːd tə lˈɛt ðə mˈɑːdəl nˈoʊ wˌɛɹ jʊɹ ˈænsɚɹ ɪz stˈɑːɹɾɪŋ. |1
+trelis_21.wav|ðə nˈʌmbɚɹ ʌv ˈɛpɑːkz, ðə lˈɜːnɪŋ ɹˈeɪt, ɐn ˈaʊtpʊt dᵻɹˈɛktɚɹi fɔːɹ jʊɹ tɹˈeɪnɪŋ mˈɑːdəl ænd ɹɪzˈʌlts, wˈɛðɚ juː wˈɔnt tə tɹˈeɪn wɪð bɹˈeɪn flˈoʊt sˈɪkstiːn ɔːɹ nˈɑːt. juː kæn sˈɛt jʊɹ skˈɛdʒuːlɚ. juː kæn dᵻsˈaɪd wˈɛðɚ tə sˈeɪv ðə mˈɑːdəl æɾə sˈɜːʔn̩ nˈʌmbɚɹ ʌv stˈɛps ʌv tɹˈeɪnɪŋ. sˈɛt jʊɹ mˈæks sˈiːkwəns lˈɛŋθ, ɡɹˈeɪdiənt tʃˈɛkpɔɪntɪŋ, ænd wˈɛðɚ tə jˈuːz ɹˌiːˈɛntɹənsi, wˌɪtʃ ɐlˈaʊz juː tə spˈiːd ˌʌp ðə tɹˈeɪnɪŋ. |1
+trelis_22.wav|nˈɛkst, juː kæn dᵻsˈaɪd wˈɛðɚ juː wˈɔnt tə jˈuːz ˈɔːɹpoʊ ɔːɹ nˈɑːt. baɪ dᵻfˈɔlt, aɪv ɡɑːt ðæt sˈɛt tə fˈɔls. ɪf jʊɹ jˈuːzɪŋ ˈɔːɹpoʊ, juː nˈiːd ɐ kˈɑːlʌm ðæts kˈɔːld tʃˈoʊzən ænd wˈʌn kˈɔːld ɹᵻdʒˈɛktᵻd. ænd juː kæn sˈɛt jʊɹ mˈæks pɹˈɑːmpt lˈɛŋθ ænd ðˈɛn ðə bˈeɪɾə. ðə bˈeɪɾə bˈeɪsɪkli wˈeɪz hˌaʊ mˈʌtʃ ʌvðə pɹˈɛfɹəns fˈaɪntˈuːnɪŋ, wˌʌts ðɪ ɪmpˈoːɹtəns ʌv ðæt lˈɔs ɹˈɛlətˌɪv tə ðə stˈændɚd ˌɛsˌɛftˈiː lˈɔs. |1
+trelis_23.wav|ɪt wɪl sˈɛt ˌʌp ðə tˈoʊkənˌaɪzɚ, sˈɛt ˌʌp ðə tʃˈæt tˈɛmplət, lˈoʊd ðə dˈeɪɾəsˌɛt, ˈiːðɚ fɹʌm jʊɹ mˈænjuːəl dˈeɪɾə ðæts ɪnðə ɹˈiːpoʊ ɔːɹ fɹʌm hˈʌɡɪŋ fˈeɪs, ænd ðˈɛn ɪt wɪl ɹˈʌn ˈɪnfɚɹəns θɹuː ˈɔːl ʌv ðoʊz sˈæmpəlz ænd pɹˈɪnt ðə ɹɪzˈʌlts ˈaʊt tə fˈaɪl. dʒˈʌst æz ɐn ɛɡzˈæmpəl, aɪ kæn ʃˈoʊ juː wɪðˌɪn tˈɛst ˈaʊtpʊt, juːl sˈiː hˈɪɹ ɐ lˈɑːɹdʒ nˈʌmbɚɹ ʌv tˈɛsts ðæt aɪ hæv ɹˈʌn. |1
+trelis_24.wav|ɪt hɐz ðə tˈɛst.pˈaɪ ænd ðə tɹˈeɪn.pˈaɪ fˈaɪl ðæt wɪl ɐlˈaʊ juː tə ɹˈʌn tˈɛstɪŋ ��nd tɹˈeɪnɪŋ. ænd aɪl dʒˈʌst bɹˈiːfli ʃˈoʊ juː ðə kənfˈɪɡ fˈaɪl. sˌoʊ æt ðə stˈɑːɹt hˈɪɹ, juːl sˈiː ðɪs pɚɹˈæmɪɾɚ ðæts nˌɑːt ɪnðɪ ʌnslˈɑːθ bɹˈæntʃ. ɪf juː sˈɛt ɪt tʊ ˈɔːɾoʊ, ɪt wɪl dʒˈʌst dˈuː stˈændɚd tɹˈeɪnɪŋ. juː kæn tɹˈeɪn ˌɔn mˌʌltɪpəl dʒˌiːpˌiːjˈuː, bˌʌt ɪt wɪl biː pˈaɪplaɪn pˈæɹəlˌɛl, sˌoʊ nˌɑːt kwˈaɪt æz ɪfˈɪʃənt. |1

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+trelis_25.wav|ðˈɛn juː kæn fˈaɪn tˈuːn lˈɑːvə, ˈaɪdə fˈɪks ænd, ɔːɹ ˈaɪdə fˈɪks ænd mˈuːndɹiːm mˈɑːdəlz. juː kæn dˈuː mˌʌltɪmˈoʊdəl sˈɜːvɚ sˈɛɾʌp wɪð tˈɛkst dʒˌɛnɚɹˈeɪʃən ˈɪnfɚɹəns. ðɛɹz ɐ wˈʌŋklˈɪk tˈɛmplət fɔːɹ ɹˈʌnɪŋ ɐn ˈaɪdə fˈɪks sˈɜːvɚ, ɪŋklˈuːdɪŋ ˌɔn ɐ kˈʌstəm mˈɑːdəl. ænd lˈæst ʌv ˈɔːl, ðɛɹ ɪz ɐ skɹˈɪpt fɔːɹ fˈaɪntˈuːnɪŋ mˌʌltɪmˈoʊdəl tˈɛkst plˈʌs vˈɪdɪoʊ mˈɑːdəlz. ðɪs ɪz bˈeɪsɪkli ɐ vˌɛɹɪˈeɪʃən ˌɔn tˈɛkst plˈʌs ˈɪmɪdʒ mˈɑːdəlz wˌɛɹ juː splˈɪt ðə vˈɪdɪoʊ ˌɪntʊ mˌʌltɪpəl ˈɪmɪdʒᵻz. |1
+trelis_26.wav|sˌoʊ ðɪ aɪdˈiə ɪz tə jˈuːz ɐ vˈɛɹi fˈæst ænd ɹˈɛlətˌɪvli smˈɔːl lˈæŋɡwɪdʒ mˈɑːdəl tə pˈɪk ˈaʊt ðə ɹˈaɪt snˈɪpɪts ænd ðˈɛn ɪŋklˈuːd ðoʊz snˈɪpɪts ɪnðə kˈɑːntɛkst əvə mˈoːɹ pˈaʊɚfəl mˈɑːdəl lˈaɪk, sˈeɪ, dʒˌiːpˌiːtˈiː fˈoːɹ. ðɛɹz ˈɔːlsoʊ ɐ fˈoʊldɚ nˈaʊ ˌɔn pɹˈaɪvəsi, wˌɪtʃ ɐlˈaʊz juː tə bˈeɪsɪkli hˈaɪd ˌɪnfɚmˈeɪʃən, lˈaɪk pˈɜːsənəl ˌɪnfɚmˈeɪʃən ˌɔn kɹˈɛdɪt kˈɑːɹdz, nˈeɪmz, ˈiːmeɪl ɐdɹˈɛsᵻz, bᵻfˌoːɹ juː sˈɛnd ɪt tʊ ɐ θˈɜːdpˈɑːɹɾi ˌeɪpˌiːˈaɪ sˌoʊ ðæt juː kæn ɹᵻdˈuːs ˌɛni dˈeɪɾə pɹˈaɪvəsi ɹˈɪsks. |1
+trelis_27.wav|lˈæst ʌv ˈɔːl, ðɛɹz ðɪ ɐdvˈænst tɹænskɹˈɪpʃən ɹᵻpˈɑːzɪtˌoːɹi. ðˈɪswˌʌn hˈɪɹ ɐlˈaʊz juː tə dʒˈɛnɚɹˌeɪt dˈeɪɾə ɪf juː wˈɔnt tə fˈaɪn tˈuːn ɐ wˈɪspɚ mˈɑːdəl ænd ðˈɛn dˈuː ðə fˈaɪn tˈuːnɪŋ. ænd ɐɡˈɛn, mˈʌtʃ ʌvðə tˈɛn tˈɪps ðæt aɪ pɹəvˈaɪdᵻd ˈɜːlɪɚɹ ɑːɹ ɡˌoʊɪŋ tʊ ɐplˈaɪ hˈɪɹ fɔːɹ tɹænskɹˈɪpʃən. ænd ðæt ɪz ɪt fɔːɹ maɪ tˈɛn tˈɪps ˌɔn fˈaɪntˈuːnɪŋ. ɪf aɪv lˈɛft ˈɛnɪθˌɪŋ ˈaʊt, plˈiːz lˈɛt mˌiː nˈoʊ bᵻlˌoʊ ɪnðə kˈɑːmɛnts ænd aɪl ɡɛt bˈæk tə juː. |1